{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \"摩拜\"需要我\n",
    "\n",
    "我们将设计人工神经网络对某地区租赁单车的使用情况进行预测，我们将这个问题分解为三个小问题：\n",
    "\n",
    "1. 输入节点为1个，隐含层为10个，输出节点数为1的小型人工神经网络，用数据的下标预测单车数量\n",
    "2. 输入节点为56个，隐含层为10个，输出节点数为1的人工神经网络，用数据库中的星期几、是否节假日、温度、湿度等属性预测单车数量\n",
    "3. 输入节点为56个，隐含层节点数为10个，输出节点数为2个的人工神经网络，用数据库中的星期几、是否节假日、温度、湿度等属性预测单车数量是大于平均值还是小于平均值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入需要使用的库\n",
    "import numpy as np\n",
    "import pandas as pd #读取csv文件的库\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "\n",
    "# 让输出的图形直接在Notebook中显示\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、准备工作：读入数据文件\n",
    "\n",
    "首先，我们读入数据，绘制图形，看看数据长成什么样子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取数据到内存中，rides为一个dataframe对象\n",
    "data_path = 'bike-sharing-dataset/hour.csv'\n",
    "rides = pd.read_csv(data_path)\n",
    "\n",
    "#看看数据长什么样子\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Y')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#我们取出最后一列的前50条记录来进行预测\n",
    "counts = rides['cnt'][:50]\n",
    "\n",
    "#获得变量x，它是1，2，……，50\n",
    "x = np.arange(len(counts))\n",
    "\n",
    "# 将counts转成预测变量（标签）：y\n",
    "y = np.array(counts)\n",
    "\n",
    "# 绘制一个图形，展示曲线长的样子\n",
    "plt.figure(figsize = (10, 7)) #设定绘图窗口大小\n",
    "plt.plot(x, y, 'o-') # 绘制原始数据\n",
    "plt.xlabel('X') #更改坐标轴标注\n",
    "plt.ylabel('Y') #更改坐标轴标注"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性回归尝试\n",
    "\n",
    "我们可以先尝试用线性回归来对曲线进行拟合，复习一下上节课学过的内容，尽管效果很差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "loss: tensor(1371.1407, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1370.5791, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1370.0353, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1368.5056, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1368.0281, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1367.5654, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1367.1176, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1366.2642, grad_fn=<MeanBackward1>)\n",
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     ]
    }
   ],
   "source": [
    "#我们取出数据库的最后一列的前50条记录来进行预测\n",
    "counts = rides['cnt'][:50]\n",
    "\n",
    "# 创建变量x，它是1，2，……，50\n",
    "x = torch.FloatTensor(np.arange(len(counts), dtype = float))\n",
    "\n",
    "# 将counts转成预测变量（标签）：y\n",
    "y = torch.FloatTensor(np.array(counts, dtype = float))\n",
    "\n",
    "a = torch.rand(1, requires_grad = True) #创建a变量，并随机赋值初始化\n",
    "b = torch.rand(1, requires_grad = True) #创建b变量，并随机赋值初始化\n",
    "print('Initial parameters:', [a, b])\n",
    "learning_rate = 0.00001 #设置学习率\n",
    "for i in range(1000):\n",
    "    ### 增加了这部分代码，清空存储在变量a，b中的梯度信息，以免在backward的过程中会反复不停地累加\n",
    "    predictions = a.expand_as(x) * x+ b.expand_as(x)  #计算在当前a、b条件下的模型预测数值\n",
    "    loss = torch.mean((predictions - y) ** 2) #通过与标签数据y比较，计算误差\n",
    "    print('loss:', loss)\n",
    "    loss.backward() #对损失函数进行梯度反传\n",
    "    a.data.add_(- learning_rate * a.grad.data)  #利用上一步计算中得到的a的梯度信息更新a中的data数值\n",
    "    b.data.add_(- learning_rate * b.grad.data)  #利用上一步计算中得到的b的梯度信息更新b中的data数值\n",
    "    a.grad.data.zero_() #清空a的梯度数值\n",
    "    b.grad.data.zero_() #清空b的梯度数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kt1zBtuAzRKNR3vj6i1j7ghfyvR/eeFKJjKc2beTCN1zEszuCDA0Nceklb2br1i3se76NT/7rv0w87txXnserzzt/ogTHjud2cPGFb2L3nl0MjwxzxVvezo7nniUajfLiM8/ijNPP5Os3fI3zzj2f97zrfXz7u9+ccUpv46a/sXHT36Yd2779Gd562RWccfqZ7G3bw+VvfivBZ7cTiZ56JK1ldSvtHe3Tjm1+ehObn940cfvSSy6jvr5+ogRHZ+dBLr3kzRw4uB+3u4SLL3wTj/31UQDq6+s5/fQr+fnN/4XdZufyy65g09NPzfqezWXT5o18+hOf5cVnnkXb889z6SWX0dffN7E+MB0U0kRERLLko//4MfY938aDD/+Jbdufob7Oyyf+16dwulw8tWkjj/51AwAP//khyssr+Pxnv4TT4WTn7p3cceftAFxy8SW4XG6u/+o3Jp73yJEjXP/Nr7HhsfU4HA7+1z9/Ak+Zh+Cz27jnvv+Zs11/fOD3OBwO/vXTn8dut7Nr905u+9WtlJSU8M5r3809997N8Mgwf9nwCK8855VccvGl3P+nP8z79z46eJSf3/JTrnzb1VRVVbFvXxu/vOOWift/cOOP+I+b/u/EDsramlqe27lj3s9//He48m1X89UvX08sGuWxJx7j8SdTu1jvufdurrnqHXzty1/HMAy2bH2a+36f2oCx2PfsYOdBfvqLn3D5ZVdQV1tH56GD/NfPb1pQm+dipHMXQq7d+NMHlwFHfv/bm4lPWYyZToZhY3nraRxq34NpZm9XkcyP+se61DfWZvX+KS1xAzA6duqRl0I3tU6aWM9c/TPT59fhdPGWqz4AUPXZD7/p6Ik/pzppIiIiIhakkCYiIiJiQQppIiIiIhakkCYiIiKSSYu8VJRCmoiIWN5YJEpZ6ezXeRSxqrLSEsYiC9/QmJMSHIFg6GrgX/w+72vGb78Q+AXgB/YBH/b7vE/NdZ+IiBQH0zSJxRN462qIRKNEY3EooOoEc3G43DhsxfP75ptT9o9h4HI6cLmcRCKxRV3TM6sjaYFgyB4Ihj4N3M70er53AX8EqoAfAr8JBEP2edwnIiJFYvDYEKHefoaHR4sqoBmGDW/zqlQlfLGcGfvHNBkeHiXcO8DgsaFT//Acsj2S9m3gVeP/+0aAQDB0BrAW+J7f540DNweCoc8AFweCoY6Z7gMeyHLbRUTEAuKJBPFTXH+yUBmGjUg0xuhYxJI17IpdJvsn2yHtB36f93AgGLpuyrHTgX3jIey4vcAZQMks980Y0gzDyNi/OI4/r/5FY03qH+tS31ib+se61DfWtpT+MebYUJDVkOb3eQ+f4rAHGD3h2AhQNsd9M2pevZZEhv+V1dyyNqPPL0uj/rEu9Y21qX+sS31jbYvpH7t99tVbVrh25whQesKxMmBojvtm1LW/jXgslrYGTmUYNppb1tLV0aZhZwtS/1iX+sba1D/Wpb6xtqX0j8PpxP+KC2a+f6mNS4NdwJpAMGT3+7zHh79OA24CDsxy34xM08z4B9k0kzpZLEz9Y13qG2tT/1iX+sbaFtM/c+34zPkEt9/nfQ7oAL4UCIZc4+vVqoANs92Xk8aKiIiIZEnOQ9q4twOvB3qBjwNv9fu8Y/O4T0RERKQg5WS60+/z3gLcMuX2PlJB7FSPnfE+ERERkUJllZE0EREREZlCIU1ERETEghTSRERERCxIIU1ERETEghTSRERERCxIIU1ERETEghTSRERERCxIIU1ERETEghTSRERERCxIIU1ERETEghTSRNLEdHgwK1owHZ5cN0VERApATq7dKVJokvV+zOZ1YLNDMoHRtQFbOJDrZomISB7TSJrIEpkOz2RAA7DZMZvXaURNRESWRCFNZKlK6ycD2nE2e+p4lmiqVWQ6nRNSCDTdKbJUo2FIJqYHtWQidTwLNNUqMp3OCSkUGkkTWSIjPozRtSEVzCD1pXBoPUZ8OOOvralWkel0Tkgh0UiaSBrYwgHMgd2pKc7RcFYCGjD7VOuxLLVBxEp0TkgBUUgTSRMjPpz9L4EcT7WKWI7OCSkgmu4UyWO5nGoVsSKdE1JINJImkudyNtUqYlE6J6RQKKSJFICcTLWKWJjOCSkEmu4UERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTERERsSCFNBERERELUkgTmYPp8GBWtGA6PLluioiIFBFHrhsgYmXJej9m8zqw2SGZwOjagC0cyHWzRESkCGgkTWQGpsMzGdAAbHbM5nUaURMRkaxQSBOZSWn9ZEA7zmZPHRcREckwhTSRmYyGIZmYfiyZSB0XERHJMIU0kRkY8WGMrg2TQS2ZwDi0HiM+nMtmiYhIkdDGAZFZ2MIBzIHdqSnO0bACmoiIZI1CmsgcjPgwHFM4ExGR7NJ0p4iIiIgFKaSJiIiIWJBCmoiIiIgFKaSJiIiIWJBlNg4EgqHXAP8GvADoAD7r93n/FAiGXgXcBJwGbAPe7/d59+asoSIiIiJZYImRtEAw5ADuBb7k93krga8A/xMIhkqBe4DvAtXAn4BbctVOERERkWyxREgD6oAawB4IhozxY2PAOuCo3+f9ld/njQI3AGcFgqEzctNMERERkeywxHSn3+ftDgRDvwDuAxJAErgSOB3YNeVxiUAw1A6cAeyc6fkMw8AwMpM/jz9vpp5flkb9Y13qG2tT/1iX+sbaltI/hmHMer8lQlogGLIDR4HLgQeBq4FbgR8Doyc8fAQom+35mlevJZFIzPaQJWtuWZvR55elUf9Yl/rG2tQ/1qW+sbbF9I/dbp/1fkuENFKjZmf5fd5Pjd++IxAMfQgwgdITHlsGDM32ZF3724jHYulvJamk3Nyylq6ONkwzmZHXkMVT/1iX+sba1D/Wpb6xtqX0j8PpxP+KC2a+f6mNS5MVgPOEYzGgl9SuTmBixG0NU6ZAT8U0zYx/kE0zqZPFwtQ/1qW+sTb1j3Wpb6xtMf1jmuas91tlgvsR4NxAMHRtIBgyAsHQ5cC5wB+B2kAwdF0gGHIBXwL2+n3eWUOaiIiISL6zREjz+7zbgXcCXwaOAF8F3ur3efcDbwb+GegDLgKuyVU7RURERLLFKtOd+H3ee0nVSjvx+BbgnOy3SERERCR3LDGSJiIiIiLTKaSJiIiIWJBCmoiIiIgFKaSJiIiIWJBCmoiISA6YDg9mRQumw5PrpohFWWZ3p4iISLFI1vsxm9eBzQ7JBEbXBmzhQK6bJRajkTQREZEsMh2eyYAGYLNjNq/TiJqcRCFNRESKVk6mHEvrJwPacTZ76rjIFJruFBGRopSzKcfRMCQT04NaMpE6LjKFRtJERKTo5HLK0YgPY3RtSAUzSAXEQ+sx4sMZf23JLxpJExGR4jPblOOxpYUl0+FJPc9oeMbgZQsHMAd2z/k4KW4KaSIiUnwyNOW4kClUIz685EAohU3TnSIiUnQyMeWoXZuSbhpJExGRopT2KccMTqFKcVJIExGRopXWKUft2pQ003SniIhIGmjXpqSbRtJERETSRLs2JZ0U0kRERNJIuzYlXTTdKSIiImJBCmkiIiIiFqSQJiIiImJBCmkiIiIiFqSQJiIiImJBCmkiIpIXTIcHs6JFl1mSoqESHCIiYnkLuXC5SKHQSJqIiFiaLlwuxUohTURErG22C5eLFDCFNBERsbbjFy6fShculyKgkCYiIpamC5dLsdLGARERsTxduFyKkUKaiIjkBV24XIqNpjtFRERELEghTaSIqBioiEj+0HSnSJFQMVARkfyikTSRIqBioCIi+UchTaQYqBioiEjeUUgTKQYqBioikncU0kSKgIqBiojkH20cECkSKgYqkp9MRxmjjnpMRyfEhnLdHMkihTSRIqJioCL55fiu7JDNDmeeo13ZRUbTnSIiIhakXdmikCYiImJF2pVd9BTSRERErEi7soueQpqIiIgFaVe2aOOAiIiIRdnCATiyh9rWs+lr36rdnUVGI2kiIiIWZsRHKI2HMeIjuW6KZJlCmoiIiIgFKaSJiIiIWJBCmoiIiIgFKaSJiIiIWJBCmoiIiIgFKaSJiIiIWJBCmoiIiIgFWaaYbSAYagFuAs4H+oAv+33e2wPB0AuBXwB+YB/wYb/P+1TOGioiIiKSBZYYSQsEQwbwO2ALUA1cA9wUCIZagbuAPwJVwA+B3wSCIftMzyUiIiJSCCwR0oDzgGXAV/w+b9zv824CXgWUA2uB7/l93pjf570ZOAZcnLumioiIiGSeVaY7zwZ2AD8OBEPXAGHgC6Tat8/v88anPHYvcAbwwExPZhgGhpGZ/Hn8eTP1/LI06h/rUt9Ym/rHutQ31raU/jEMY9b7rRLSqoE3AZ8CVgKvB+4GvgOMnvDYEaBstidrXr2WRCKRgWZOeY2WtRl9flka9Y91qW+sTf1zagnDTdReiSsxiN2M5KQN6htrW0z/2O2zr96ySkiLAAf8Pu+/jd9+MBAM/RUwgNITHlsGDM32ZF3724jHYulvJamk3Nyylq6ONkwzmZHXkMVT/1iX+sba1D8zS9adTbL5dWCzQzKBretRbL1bs/b66htrW0r/OJxO/K+4YOb7l9q4NNkDLAsEQ4bf5zXHj9mBo8CaQDBk9/u8x4fGTiO1C3RGpmlm/INsmkmdLBam/rEu9Y21qX+mMx2eyYAGYLOnbg/swogPZ7ct6htLW0z/mKY56/1WmeB+mNRo2vWBYMgeCIb+Dng1cC/QAXwpEAy5AsHQdaR2eW7IUTtFRKSYlNZPBrTjbPbUcZEMs0RI8/u8I6TWoZ1Hqkbaj4B3+X3e/cDbx+/rBT4OvNXv847lqq0iIlJERsOQPGGNczKROi6SYVaZ7sTv8+4GLjzF8X2kQpqIiEhWGfFhjK4NmM3rJtakGYfWZ32qU4qTZUKaiIiIFdnCAcyB3akpztGwAppkjUKaiIjIHIz4MBxTOJPsssSaNBERERGZTiFNRERExIIU0qQomQ4PZkULpsOT66aIiIic0oxr0gLB0Cq/z3sgm40RyYZkvX/6Tq2uDdjCgVw3S0REZJrZRtJ2BIKhj2WtJSJZYDo8kwENwGbHbF6nETUREbGc2ULat4HvBIKhvwaCoRdlq0EiGaXq4SIikidmDGl+n/cG4CxS1898JhAMfTEQDM1+uXYRq1P1cBERyROz1knz+7ztwGWBYOitwHeBqwPB0NeBsRMed3/mmiiSPqoeLiIi+WJexWz9Pu/vAsFQDfAT4Lcn3G0CGmGTvKHq4SIikg/mDGmBYOgc4D+AlwDfB/6PLnAu+U7Vw0VExOpmK8FRBXwL+HtgE/Byv8+7I1sNExERESlms42k7Rm//2N+n/cnWWqPiIgUGdPh0fKDNNF7WVhmC2l/Bj7h93l7stUYEREpLiounT56LwvPjCHN7/O+M5sNERGR4jJjcemB3RoFWiC9l4VJ1+4UEZHcUHHp9NF7WZAU0kREJDdUXDp99F6mnd2RzHUTFNJERCQ3jheXnggXKi69aHov08PpTrLiRaO84pIjXHRdL+VV8Zy2Z17FbEVERDJBxaXTR+/l4rjLEjS2RGhsjVDTHCMybKO7w82mP1YxdDS3tfoV0kREJKdUXDp99F7OT2lFgsbWVDCrbogxfNROd7ubXZvKORp2AEaumwgopImIiEgRKK+KTwSzZfVxBvscdLe7CT5WwdCAHasEs6kU0kRERKQAmVTWpYJZU2uE8uoEAz0Ouva5CTxSycig9SOQ9VsoIiIiMi8m1Q1xGlvHaGyNUFqepP+wk/07SunucDM2nNs1ZgulkCYiIiJ5yzBMappjqanMlgiukiS9h1y0bfXQ0+EmOpa/hSwU0kRERCSv2GwmdSuiNLZGaGiJYLebhDvd7NxYTuiAi3g0f4PZVAppIiIiYnl2R5L6lVEa10TwroqCCaEDLoKPVRA+6CYRt97C/6VSSBMRERFLcriSNKxO7cisXxklETPo7nCz9ZFK+g65SCau04pCAAAgAElEQVQLL5hNpZAmIiIiluEqSdIwXiqjrjlKdMxGd7ubzQ9U0X/YiWkWdjCbSiFNREREcqrEM1lctqYxxsgxG90dJezd4uFIj3WKy2abQpqIiIhkXVllnKY1qWBW5Y1zrD9V9X/HE+Uc6y/eYDaVQpqIiIhkgUlFTWKihlllbYIj4VTV/2fWVzJ8RJHkRHpHREREJENMqryTl2Mqq0ww0O2kc3cp3e1uRofyq7hstimkiYiISPoYJjWNsYlg5i5L0tfl5PltZfTsdxEZUTCbL4U0ERERWRLDZlK3fLK4rMNp0tvpYvdmD6H9bmKRwigum20KaSIiIrJgNodJ/YooTWvG8K6KYthSxWV3PFFB6ICLREzBbKkU0kRERGReHM4k3tXR8eKyEZIJg579qYX/vZ0ukgntyEwnhTQRERGZkbMkVfW/qTVC7YoosYiNnnY3Wx6sou+wE7PAq/7nkkKaiIiITOMum1JctinG2HCq6n/bM1UM9DihiKr+55JCmoiIiFBakQpmTa1jVDfGGTqSKi67a2M5R3tVXDYXFNJERESKVHnVeA2zNRGW1cU52psqLrv90UqGjthRMMsthTQREZGiYVJZF6dpfCqzvDrBQLeDrr0lBB52MTKoWGAl6g0REZGCZlI9pbhsqSdJ32EnHTtSVf9VXNa6FNJEREQKjGEzqWmK0TReXNZVkqS308XeLR569ruJjamGWT5QSBMRESkANrtJ3Yrxqv+rI9jsJuGDbnZuLCd0wEU8qmCWbxTSRERE8pTdmcS7MhXMvKuimCb07Hex/dFKwp0uknEt/M9nCmkiIiJ5xOFK0tCSWl9WvyJKPGbQ0+Em8EglvYdcKi5bQBTSRERELM7hirLqjBEaWkapbY4RGU0Vl910fxUD3U5MFZctSAppIiIiFlRanqChNXU5purGbkaO2el+3s2ep8s5ElJx2WKgkCYiImIRnmWTxWWr6uMM9tnp6Sgh3OFjX/AQpmnmuomSRQppIiIiOWNSWRufqGFWUZPgSMhB9/NunvlzJcNHHRiGjeWt5aRGzhTSiolCmoiISFaZVHnjNK6J0NgyRlllkv5uJwd2ltLd4WZsSMVlJUUhTUREJMMMI1VctnG8uKy7NElfl4vnt3no7nATHVUNMzmZQpqIiEgG2GwmtcujE8HM4TQJH3Sxe1Oq6r+Ky8pcLBXSAsHQmUAAOMvv87YFgqFXATcBpwHbgPf7fd69uWyjiIjITGwOE+/KyERxWcOAngMunn28gvABF4m4gpnMn2VCWiAYcgA3A+7x2yXAPcBngN8CnwduAc7PURMXzHR4oLQeRsMY8eFcN0dERDLA4UriXTVe9X9lhETCoGe/m2f+kioum0yoVIYsjmVCGvAF4HHgleO3Xw8c9fu8vwIIBEM3AJ8OBENn+H3enTlq47wl6/2YzevAZodkAqNrA7ZwINfNEhGRNHCVJGlYnSqVUbc8SnTMRk+Hm80PVtF/2Kmq/5IWlghpgWDopcC1wDnAp8YPnw7sOv4Yv8+bCARD7cAZwKwhzTAMDCMzQ8rHn3e25zcdnsmABmCzp24f2YMRH8lIuyRlPv0juaG+sTb1z9zcZQkaW8dobI1Q0xhldMhOd4ebtq3lHOlxMrW4rJHGjKa+sbal9I8xxwcl5yEtEAy5SE1z/qPf5x0NBEPH7/IAoyc8fAQom+s5m1evJZFIpLWdJ71Gy9oZ7xt11BOynbCF2mantvVsSuPhjLZLUmbrH8mtXPRNwnATtVfiSgxiNyNZf/18onNnOlfpKMu8vSzz9uKpOsbYUClHQw3s3VTH6LFU7TJPGXhaM98W9Y21LaZ/7PbZy63kPKQB/xvY4Pd5nzjh+AhQesKxMmBorifs2t9GPBZLU/OmMwwbzS1r6epowzSTp3yM6eiEM8+ZHEkDSCboa9+qkbQMm0//SG7kqm+SdWeTbH7dxNIDW9ej2Hq3Zu3184XOneNMyqvHi8u2jFFZF+dor4POvSX0tNcxdMQBxIDDWWuR+sbaltI/DqcT/ysumPn+pTYuDa4CmgLB0AenHAsAHyG1qzN1IBiyA2uYMgU6E9M0M/5BNs3kzK8RG8Lo2jB9Tdqh9RAbUq3oLJm1fySnstk3psMzGdAAbPbU7YFd2swzg+I8d0yW1Y0Xl22NUF6VoL/bSefeErofcjN6bOpoR+7em+Lsm/yxmP6Z6zJfOQ9pfp/39Km3A8GQCfiBQ8APAsHQdcCvSO3u3Ov3eecMaVZgCwcwB3Zrd6dILpXWTx/RhtTt0no4pnOyqBkm1Q2xicsxlXiS9B920hFMVf2PjKjqv+RezkPaTMbXp72ZVJ20fweeAa7JbasWxogP64tAJJdGw5BMnLT0gFGtDS1Ghs2ktvl4cdkoTneS3k4Xe7ekisvGxrQwX6zFciHN7/MaU/7/FlI7PkVEFsyID59y6YFGtouHzW5Sv2Ky6r9hMwkfcLPzyXJCB1zEYwpmYl2WC2kikl+sXrRZSw+Kj905pbjsqihmEnr2u9m2oZJwp4tkXDXMJD8opInIouVL0WYtPSh8Tvf04rLxmEFPh5stD1XS1+VScVnJSwppIrIoMxVtNgd2a7RKssJdmqBhfOF/bXOMyIiN7nY3m7ZV0d/tBFPBTPKbQpoUFNNRxqijPlWrLjZnST1ZigXunFTfSDqUlicmdmRWN8YYGbRz+Hk3uzeVczTsYGrVf5F8p5AmBeP41FvIZoczz7Hs1FvBWMDOSfWNLIVn2WQNs6r6OIN9drrbS3j28QqO9dtRMJNCpZAmBUFTb9k3352T6htZOJPK2vjEiFlFTYKBHgeH97nZ+kglI4P66pLioE+6FAYVLU27+ezanNfOSfWNzItJVUOcpvELmJeWJ+nvdnJgZynd7W7GhlVcVoqPQpoUBhUtTauF7Nqcc+ek+kZmYBgmNU2TVf9dJUl6D7lo2+qhp8NNVMVlpcgppElBUNHS9En39KT6Rqay2UzqjheXXR3B7jQJH3Sx66lU1f94VMFM5DiFNCkYtnAAjuyhtvVs+tq3agfhYmVgelJ9U9zsDpP6lanRMu/qKACh/S6Cf60gfNBNQsVlRU5JIU0KihEfoTQexoiPYOa6MfkqQ9OTC+kbq1/FQObmcCVpWB2lsXWM+pVREjGDnv2phf99h1wkVVxWZE4KaSIyTa6nJ/PlKgZyMldJkoaW1IhZ3fIo0bFUcdnND1TRf9iJqeKyIguikCYiJ8nV9S5VriP/lHgmi8vWNMYYHbJxuL2EvQEPR3pUXFZkKRTSROSUcnK9S5XryAtllZM1zKob4hzrt9Pd7ua5J8sZ7FMwE0kXhTQRsQ6V67Aok4qaBI3jNcwqaxMcDTs43O5m24ZKho/oq0QkE3RmiYhl5Ho9nExlsqw+NWLWtCZCWWWCgW4nnbtTxWVHh1RcViTTFNIsQDvZRCblaj2cAJjUNEVpaBmlsTWCuyxJX5eT57eX0tPhJjKiYCaSTQppOaadbCIny8l6uCJl2Exqm6M0rYnS9IKN2Gwxejtd7NmcKi4bi6i4rEiuKKTlkHayiUgu2Owm9StTNcwaVkcxbBA+4KZr1wvYtaWfeDTXLRQRUEjLLe1kE5EscTiTeFelLsdUvyqCmTTo6XDzzPpKejtdmEk7y1u9JGJHgGSumyt5SEt30k8hLZe0k01EMsjpnlJcdkWUeMSgu8PNloeW0dflwpxS9d9Q1QxZAi3dyQyFtBzSTjYRSTd3WYLG8WBW0xwjMmzjcLubfc9UMdDjBFX9lzTT0p3MUUjLMe1kE5GlKq1ITCkuG2P4aKq47K6nyjnaq+KycrK0Tk1q6U7GKKRZgHayichClVdNVv1fVh/naK+D7nY3wccqGBqwo2AmM0n71KSW7mSMQpqISF4wqaybDGYV1QkGehx07XMTeKSSkUH9OZe5ZWJqUkt3MkdntYiIZZlUN8QmgllpeZL+w07270gVlx0bVnFZWaAMTU1q6U5mKKSJiFiIYTOpaYrR1BqhoSWCqyRJ7yEXbVs99HS4iY6puKwsQQanJrV0J/0U0kREcsxmN6lbHqVxTYSG1RHsdpNwp5udG8sJHXARjyqYSXpoajK/KKSJiOSA3ZGkflWUptYI3lVRTBNCB1wEH6sgfNBNIq6F/5IZmprMHwppIpK38q3CucOVpGF1hMY1EepXREnEUsVlA49U0nfIRTKpYCbZoanJ/KCQJiJ5KV8qnLtKkxPFZWubo0THbHS3u9n8QBX9h52YKi4rIjNQSBORvGP1Cucl5eNV/9dEqGmMMTJoo7ujhD1PezgSUnFZEZkfhTQRyT8WrHBeVhmnaU1qxKzKG+dYf6rq/47HyznWr2AmIgunkCYi+ccSFc5NKmoSNLaO0dgaobI2wZFQqur/M3+pZPio/ryKyNLor4iI5J3clREwWVY/OWJWVplgoNvJwd2l9LS7GR1ScVkRSR+FNBHJS1krI2CY1DROVv13lybp63Ly/LYyejpcREYVzEQkMxTSRCRvZaqMgGEbLy47XvXf4TQJd7rYvclD6ICbWETFZUUk8xTSREQAm8OkfkWUxtYxGlZHMYxUcdkdj1cQOuAiEVcwE5HsUkgTkaLlcCbxrk6NmNWvjJBMGPTsd/PM+kp6O10kE9qRKSK5o5AmIkXFWZKkcfV4cdkVUWJjNno63Dz94HhxWVX9FxGLUEgTkYLnLktMLPyvbYoxOpyq+t+2tYqBkBNU9V9ELEghTUQKUmlFgqbWCI1rxqhuiDM0kCouu3NjOYO9Ki4rItankCYiBcKkvHpyxGxZXZyjvQ66n3ezfUMlQ0f0505E8ov+aolIHjNZVhencU2ExpYI5dUJBroddO0tYctDbkaPqYaZiOQvhTQRyS+GSXXDZHHZUk+SvsNOOnaU0t3uJjKiYCYihUEhTUQsz7CZ1DbFaFwzRkNLFJc7SW+ni71bPIT2u4mOqYaZiBQehTQRsSSb3aRuxXjV/9URbHaT8EE3O/9WTuiAi3hUwUxECptCmohYht2ZxLsqFcy8q6KYSejZ72L7o5WEO10k49qRKSLFQyFNRHLK6U7iXR2hqTVC3Yoo8ZhBT4ebwMOV9B5yqbisSA6ZDg+U1sNoOHWtXMkqhTQRyTp3aYKGltR1MmubY0RGU8VlN91fRX+3isuKWEGy3o/ZvA5sdkgmMLo2YAsHct2soqKQJiJZUVKeoG5VJyvP6qO6McbIYKq47O7N5RwNq7isiJWYDs9kQAOw2TGb12EO7NaIWhYppIlIxniWxSdKZVR544weG6Zzj4tnH6/gWL8dBTMRiyqtnwxox9nsqePHFNKyRSFNRNLIpLJ2MphV1CQ4EnJw+Hk329ZXUVVzJofa92CayVw3VERmMxqGZGJ6UEsmUsclaywT0gLB0FuBbwIrgT3AJ/w+7+OBYOhS4IfACuAx4P1+nzeUu5aKyHQmVd7jVf/HKKtM0n/YyYGdpXR3uBkbSv2RNwwbVTU5bqqIzIsRH8bo2jB9Tdqh9ZrqzDJLhLRAMLQG+CVwOfBX4J3AfYFg6GzgTuAK4G/Av5MKbO/OUVNFBDAMk5qmyar/rpIkvV0u9m3z0NPhJjqqGmainYH5zhYOYA7sVh/mkCVCGrAa+Knf5310/PYdgWDox8B1wJN+n3c9QCAY+iJwKBAM/ZPf5x3MTVNFipPNZlK7PErTmlRxWbvTJHzQxa6nPPTsd6u4rEyjnYGFwYgPaw1aDlkipI2HsPXHbweCoXOBcqAG2DXlceFAMDQCrAVmPNsNw8AwMvOFcfx5M/X8sjTqn/SyO5LUr4zS0DKGd3UEAwgdcPPs48sIH3SRiE++z8YcewDUN9aWzv6ZaWcgR/ZgxEeW/PzFRueOtS2lf4w5/nBaIqRNFQiG1gJ3A18BXgScuP5sBCib7TmaV68lkUhkpoHHX6NlbUafX5ZG/bN4Nkecyro+lnl7qawbIJmwMRiu5eCOOob6qzGTNuxA48rFPb/6xtrS0T+jjnpCp9gZWNt6NqVxLTxfLJ071raY/rHb7bPeb6mQFgiGXgXcB/yn3+f9biAY+jeg9ISHlQFDsz1P1/424rFYRtpoGDaaW9bS1dGmHWoWpP5ZHFdJkoaWMRpaxqhbHiUasdHT7mb35mX0H3ZhmgbQN/7f4qhvrC2d/WM6OuHMc07aGdjXvlUjaYugc8faltI/DqcT/ysumPn+pTYuXcZ3cd4JfNrv8/5s/PAu4M1THuMlNQ3aNttzmaaZ8Q+yaSZ1sliY+mduJZ4EjS0RGtdEqGmMMTqUqvrfFqhmoGdqcVlz/L/0UN9YW1r6JzZ0yp2BxIbS+EkqPjp3rG0x/WOas58RlghpgWBoNXAXcJ3f5717yl33At8KBENvAh4FbgDu8/u8s46kiciplVVO1jCrbogzNGDncLub5/5WzmCvqv5L+mhnoMjSWSKkAZ8EPMCtgWDo1inHLwOuYbJO2l+B92e/eSL5yqS8OkHTmlQwq6yNczTsoLvdzfYNlQwdscqfAClE2hkosjSW+Avt93k/AXxiloecma22iOQ/k2X1kyNm5VUJ+ruddO4uobvDzeix2ReqioiINVgipInIEhkm1Q2x1IhZSwS3J0l/l5OOYKrqf2REwWy+5luAVYVaRSTTFNJE8pRhM6ltjtLYGqGhJYrTnaS308WeLanisrEx1VRaqPkWYFWhVhHJBoU0kTxis5vUrzgezCIYNggdcPHck+WED7iIxxTMFmumAqzmwO5pI2XzfZyIyFIppIlYnN2ZxLsqFcy8q6KYSejZ72bb+krCnS6SCe3ITIvS+ul1vSB1u7R++uL3+T5ORGSJFNIWyHSUMeqoTxVrjKkSiGSG052kYXWqhlnd8ijxqEF3h5stD1XS1+XCTCqYpd1oGJKJkwqwMhpe3ONERJZIIW0Bjq9DCdnscOY5WociaeUuS9DQEqGpNUJNc4zIsI3uDjebtlXR3+0EU8Esk4z48CkLsJ44hTnfx4mILJVC2jxpHYpkQml5IlUqY02E6oYYw0ftdLe72bWpnKNhFZfNtvkWYFWhVhHJBoW0+dI6FEkTT1WcpvEaZsvq4wz2OTj8vJvgYxUMDdhRMMut+RZgVaFWEck0hbT50joUWTSTyto4jeNV/yuqEwz0OOja5ybwSCUjgzoNRUTkZPp2mCetQ5GFMaluiNPYOkZja4TS8iT9h50c2JEqLjs2rOKyIiIyO4W0BbCFA3BkD7WtZ9PXvlW7O7MkXyq7G4ZJTXMstcasJYKrJEnvIRdtWz30dLiJqrisiIgsgELaAhnxEUrjYYz4CGauG1MErF7Z3WYzqZtSXNZuNwl3utn1VDk9+13EowpmIiKyOAppYllW3VFrdySpXxmlcU2quCxAaL+L4GMVhA+6ScS18F9ERJZOIS2P5Mu0X9pYaEetwzVeXLY1Qv3KKImYQc9+N1sfqaTvkIukisuKiEiaKaTlCatP+2VEjnfUukqSNIyXyqhrjhIds9Hd7mbzA1X0H3ZiqrisiIhkkEJaHrDqtF+m5WJHbYlnvLhsa4SaxhijQ6lgtndLNUd6VFxWRESyRyEtH1ho2i/bslHZvawyTmNrhKY1Eaq8cY71p6r+P/dkOYN9CmYiIpIbCmn5oMgL6aa/srtJRU1iooZZZW2CI2EH3e1unllfyfARnRYiIpJ7+jbKAyqkmw4my+onR8zKKhMMdDvp3F1Kd7ub0SEVlxUREWtRSMsTuqDzIhgmNY2xiTVm7rIkfYecPL+tjJ79LiIjCmYiImJdCml5RBd0npthM6mo7eesFUdpaBnD4TQJd7rYvdlDaL+bWETFZUVEJD8opEneszlM6ldEaWwdo2F1FJs9TM9+FzseryB00EUipmAmIiL5RyFN8pLDmcS7KlX1v35lhGQiVVx224ZluBxn0bmvDdNM5rqZIiIii6aQJnnDWTJZ9b9uRZR4xKC7w82WB6voO+zETBoYho3lrRo5ExGR/KeQJpbmLptSXLYpxthwqrjsvmeqGOhxgqr+i4hIgVJIE8sprUjQ1BqhsXWM6sY4Q0dSxWV3bSznaK+Ky4qISHFQSBNLKK9K1TBrXBNhWV2co72p4rLbH6tkaMCOgpnkK9PhUekckSl0TsyfQprkiEllXXx8xCxCeXWCgR4HXW1uAg9XMjKoj6bkv2S9f3oR6q4N2MKBXDdLJGd0TiyMvgkli0yqG2I0rkkFs1JPkv7DTjp2lNLT4WZsWMVlpXCYDs/klxGAzY7ZvA5zYLdGD6Qo6ZxYOIU0ySjDZlLTFKOpNUJDSwRXSZLeQy7atnjoPlRN1GjUkLcUptL66dfbhdTt0vq8LkqtqSpZtEWcE8X+eVNIk7Sz2U3qlqdqmDWsjmC3m4QOutm5sZzQARfxqC015P2CdRrylsI1GoZkYvqXUjKROp6nNFUlS7LAc0KfN4U0SRO7I0n9qiiNrRG8q6JgQuiAi+BjFYQOuknGJxf+a8hbioERH8bo2jD9S+bQ+rz9jOu8laVayDmhz1uKQposmsM1WVy2fmWUeMygp8PN1kcq6TvkIpmcYUdmgU4DiZzIFg5gDuwujOkanbeSBvM+J/R5AxTSZIFcpUkaW1LBrLY5SmQ0VVx20/1VDHQ7MedTXLYAp4FEZmLEhwvjS0XnraTJvM4Jfd4AhTSZh5LyRCqYrYlQ0xhjZNBGd3sJe572cCS08OKyhTYNJFIMdN5KNunzlqKQJqfkWRafuBxTlTfOYJ+d7g43Ox6v4Fj/0ovLFtQ0kEiaWH0nm85bySZ93hTSZIJJRU2cxjURmlojVNQkOBJKVf1/5i+VDB9N/0elYKaBRNIgX3ay6byVbCr2z5tCWlEzqfJOjpiVVSbo73ZyYGcp3R1uxoZUXFYkG7STTURORSGtyBiGSXVjjKY1qeKy7tIkfV0u9m0ro6fDTXTUlusmihQf7WQTWRKrLxVYLIW0ImCzmdQuT9Uwa2iJ4HCahA+62L3JQ89+N/GogplITmknm8ii5ctSgcVQSCtQNoeJd2VkorisYaSKyz77eAXhAy4ScQUzEavQTjaRxSn0pQIKaQXE4UriPV71f2WERMKgZ7+bZ9ZX0tvpIplY2o5MEckc7WQTWYQCXyqgkJbnXCVJGsaLy9YtjxKN2Ohpd/P0g1X0HXZizlT1X0Qsp9h3soksWIEvFVBIy0PusgSNralSGTVNMUaHU1X/2wJVDIScMJ+q/yIiInmu0JcKKKTlibLKyVIZ1Q1xhgbsdLe7eW5jOYO9C6/6LyIiUggKeamAQpplmZRXJyaC2bK6OEfDDro73GzfUMnQEet2XaFuhRaxCp1jItMV6lIB637TFyWTZXWpqv+NrRHKq1LFZQ/tLWHLQ25Gj1m/uGwhb4UWsQKdYyLFQyEt1wyT6obYxIhZiSdJ/2EnHcFU1f/IiPWD2XGFvhVaJNd0jokUF4W0HDBsJrVNMRrXjNHQEsXpTtLX6WLvllRx2diY9WqYzWt6pcC3Qovk3CLOMU2NikzKt/NBIS1LbHaTuhXjVf9XR7DZTcIH3Ox8spzQARfxmPWC2XHznl4p8K3QIjm3wHNMU6Mik/LxfFBIyyC7c0px2VVRzCT07Hez/dFKwgfzo7jsQqZXCn0rtEiuLeQc09SoyKR8PR8U0tLM6U7SsHq8uOyKKPGoQXeHmy0PVdLX5cpacdn5DunO+bgFTq8U8lZoESuY9zm2wHPXdJQx6qjHdHRCbCgDLRfJoTxdKqCQlgbu0gQNLVEa14xR2xwjMpIqLrvp/ir6u7NfXHa+Q7rzetwipjALdSu0iFXM6xxbwLl7/G9ByGaHM8/Ji2kgkQXJ06UCeRHSAsHQq4CbgNOAbcD7/T7v3ly2qbQ8TkPLWKq4bGOMkUE7h593s3tTOUfDuSsuO98h3fk+TlOYIvlpvuduvk4DiSxEvi4VsHxICwRDJcA9wGeA3wKfB24Bzs9Fe6oaorzwVQHKKocY7EtV/X/28QqO9duxRNX/+Q7pLmDoV1OYIvlpXueudmVLkcjUUoFMsnxIA14PHPX7vL8CCARDNwCfDgRDZ/h93p3Zbkx0xMaRnno2P1DC0BEL7sic75DuAod+NYUpkp/mPHe1K1uKSLqXCmRaPoS004Fdx2/4fd5EIBhqB84AThnSDMPAMDIToEaHHIQ7VjIy2IZhJDPyGkthJEah61GSza+bGNK1dT2KLTEKU96T+T4u3xzv90z1vyye+saaCvVvQSHRuZNdCz0nltI/hjH7DFw+hDQPMHrCsRGgbKYfaF69lkQikdFGNbeszejzL80wiaE/E7VX4koMYq+IQMVpS3hc/rF2/xQ39Y0VFe7fgkKicyebFn5OLKZ/7PbZryqUDyFtBCg94VgZMOMe8a79bcRjsYw0xjBsNLespaujDdO03khasVP/WJf6xtrUP9alvrG2pfSPw+nE/4oLZr5/qY3Lgl3Ah47fCARDdmANU6ZAT2SaZsY/yKaZ1MliYeof61LfWJv6x7rUN9a2mP4xTXPW+/MhpK0HagPB0HXAr0jt7tzr93lnDGkiIiIi+c7yqxD9Pu8o8Gbgn4E+4CLgmpw2SkRERCTD8mEkDb/PuwU4J9ftEBEREckWy4+kiYiIiBQjhTQRERERC1JIExEREbEghTQRERERC1JIExEREbEghTQRERERC1JIExEREbEghTQRERERC8qLYrYL5XA4M/bchmFgt9txOJ1zXnNLsk/9Y13qG2tT/1iX+sbaltI/c+UVo5A6/MafPrgSOJDrdoiIiIgswKrPfvhNB088WGgjaZ3AKmAw1w0RERERmYdKUvnlJAU1kiYiIiJSKLRxQERERMSCFNJERERELEghTURERMSCCm3jQEYFgqFXATcBpwHbgPf7fd69uW2VBIKhq4F/8fu8rxm//ULgF4Af2Ad82O/zPpXDJhalQDD0VuCbwEpgD/AJv8/7eCAYuhT4IbACeIzUeRTKXVNovzkAAAV7SURBVEuLTyAYejfwNaAJ2Emqb57Q3zhrCQRDZwIB4Cy/z9um/sm9QDD0GVJ/16JTDp9G6u9c2vtGI2nzFAiGSoB7gO8C1cCfgFty2aZiFwiG7IFg6NPA7YAx5a67gD8CVaTCwG8CwZA9B00sWoFgaA3wS+CjTPbDfYFgaDVwJ/ARoJbUjqYf5qqdxSgQDL0I+H/AO/w+bznwU+C3+htnLYFgyAHcDLjHb6t/rOFlwKf9Pm/58f+AfjLUNwpp8/d64Kjf5/2V3+eNAjcAZwWCoTNy3K5i9m3greP/C8B4f6wFvuf3eWN+n/dm4BhwcW6aWLRWAz/1+7yP+n3epN/nvQNIAtcBT/p93vV+n3cM+CJwdSAYqsxhW4uK3+fdDTT7fd4t/7+9uwnRqgoDOP6PRNNsY3QKd0maZRftBtmictUihEIL+yDCyGzRIpKwTEcqBIMooqBVGRK0SoiyjZsK0kWLE3YkJkqsIJJTggszQaUW5w68vc7ILJy5h97/D4Z57zObh3m49z7v+bg3pjyPclM5gde42mwDvh44tj51WEUZKRs0Y7WxSZu+5cD4xEHbhPPAMcATpD9vtk24m1KHCcuBo20Tzg3EfsQ6zaquCXt+4jimfAewEFjEf8+jP4DTlMZas6RtwqmY8q2U//2rwBa8xlUjprwSeAjYMRC2Pj3rRjNvBLbGlHNM+XBMeS0zWBubtOm7Evh7KHYaWNBDLgLaJvw+Sdg6VSamfAOwDxjD+tTkCHAFZer5Y+AqrE3vYspzKdOcT7dNGKyH507/AnAIeIeypnY7ZXnNQmaoNm4cmL7TwPyh2ALgVA+5aGrWqSLdQudPgXfbJrweU34b61OFtglnu48fxJS3AGewNjXYCXzZNuHgUNxrW8/aJvwKrBkI7Y8pfwH8xQzVxpG06Run7NoAyqJ1YAkDQ5yqwjiwZGijwDKs06zrdnEeALa3TXilCw+fR4HyLfSn2c9wNMWU18aUPx8KzwV+wGtcDR4Enowpn4wpn+xiETiO9elVTHllTHnrUHge5QvOjNTG10JNU0x5PmWO+UXgo+73/W0Tbus1MRFT3ghsaptwZ3d8mDK99hrwKGW79JJuobpmQbeL8wiwsW3CvoH4YsojHzYAX1GmDa5um7C+l0RHUEz5OsrNYxPwCWW6cwuwAq9x1Ykp/wMsBX7D+vQqpnw95br2CLAfeAB4n7JO7VtmoDaOpE1TtzZgLfAMZSfUPZQbjeqznrLb5k/gWcrJYoM2u56jrKHZG1M+NfFD+ba5gfLYjQwsBjb3l+boaZtwHFhHWSN4ovt8r9e4ulmf/rVNOEZp0HZTnhqwHbivWx89I7VxJE2SJKlCjqRJkiRVyCZNkiSpQjZpkiRJFbJJkyRJqpBNmiRJUoVs0iRJkipkkyZJQ2LKN8eUz8SUd0zyt/diyuPdA64lacbYpEnSkLYJ3wMvA2Mx5Vsm4jHldcDjwGNDL7+WpEvOh9lK0iS69+8dAi4HVgPXAt8Bb7VN2NVnbpJGg02aJE0hpnwT5Z18Y8AaYBFwV9uE870mJmkk2KRJ0kXElF8AdgFngFVtE472nJKkEeGaNEm6uM+AOcDPwC/9piJplNikSdIUYspzgL2UtWlLgW39ZiRplNikSdLUdgLLgIcpU55jMeWV/aYkaVS4Jk2SJhFTXg0cBDa3TdjTjap9A1wG3N424WyvCUr633MkTZKGxJQXAB8CB9om7AFom3AOeAJYAVzwkFtJutRs0iTpQm8A1wBPDQbbJhwGdgMvxZTbPhKTNDqc7pQkSaqQI2mSJEkVskmTJEmqkE2aJElShWzSJEmSKmSTJkmSVCGbNEmSpArZpEmSJFXIJk2SJKlCNmmSJEkV+hfvif9eTkrh+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制图形，展现线性回归的效果，结果惨不忍睹\n",
    "\n",
    "x_data = x.data.numpy() # 获得x包裹的数据\n",
    "plt.figure(figsize = (10, 7)) #设定绘图窗口大小\n",
    "xplot, = plt.plot(x_data, y.data.numpy(), 'o') # 绘制原始数据\n",
    "\n",
    "yplot, = plt.plot(x_data, predictions.data.numpy())  #绘制拟合数据\n",
    "plt.xlabel('X') #更改坐标轴标注\n",
    "plt.ylabel('Y') #更改坐标轴标注\n",
    "str1 = str(a.data.numpy()[0]) + 'x +' + str(b.data.numpy()[0]) #图例信息\n",
    "plt.legend([xplot, yplot],['Data', str1]) #绘制图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、第一个人工神经网络预测器\n",
    "\n",
    "我们构建一个单一输入，10个隐含层单元，1个输出单元的人工神经网络预测器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 慢速版本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: tensor(2382.1055, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1013.3519, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1011.7914, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1011.3608, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1011.1914, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1011.1096, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1011.0645, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1011.0372, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1011.0190, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1011.0061, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9968, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9897, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9842, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9797, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9760, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9726, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9694, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9675, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9654, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9640, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9625, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9610, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9598, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9587, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9580, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9571, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9565, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9556, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9548, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9546, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9541, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9536, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1010.9501, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9500, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1010.9495, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1010.9490, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1010.9488, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1010.9484, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9482, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9482, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1010.9474, grad_fn=<MeanBackward1>)\n",
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      "loss: tensor(1010.9473, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9472, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9473, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9473, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9472, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9473, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9472, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9471, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9471, grad_fn=<MeanBackward1>)\n"
     ]
    }
   ],
   "source": [
    "#取出数据库中的最后一列的前50条记录来进行预测\n",
    "counts = rides['cnt'][:50]\n",
    "\n",
    "#创建变量x，它是1，2，……，50\n",
    "x = torch.FloatTensor(np.arange(len(counts), dtype = float))\n",
    "\n",
    "# 将counts转成预测变量（标签）：y\n",
    "y = torch.FloatTensor(np.array(counts, dtype = float))\n",
    "\n",
    "# 设置隐含层神经元的数量\n",
    "sz = 10\n",
    "\n",
    "# 初始化所有神经网络的权重（weights）和阈值（biases）\n",
    "weights = torch.randn((1, sz), requires_grad = True) #1*10的输入到隐含层的权重矩阵\n",
    "biases = torch.randn((sz), requires_grad = True) #尺度为10的隐含层节点偏置向量\n",
    "weights2 = torch.randn((sz, 1), requires_grad = True) #10*1的隐含到输出层权重矩阵\n",
    "\n",
    "learning_rate = 0.0001 #设置学习率\n",
    "losses = []\n",
    "for i in range(1000000):\n",
    "    # 从输入层到隐含层的计算\n",
    "    hidden = x.expand(sz, len(x)).t() * weights.expand(len(x), sz) + biases.expand(len(x), sz)\n",
    "    # 将sigmoid函数作用在隐含层的每一个神经元上\n",
    "    hidden = torch.sigmoid(hidden)\n",
    "    #print(hidden.size())\n",
    "    # 隐含层输出到输出层，计算得到最终预测\n",
    "    predictions = hidden.mm(weights2)#\n",
    "    #print(predictions.size())\n",
    "    # 通过与标签数据y比较，计算均方误差\n",
    "    loss = torch.mean((predictions - y) ** 2) \n",
    "    #print(loss.size())\n",
    "    losses.append(loss.data.numpy())\n",
    "    \n",
    "    # 每隔10000个周期打印一下损失函数数值\n",
    "    if i % 10000 == 0:\n",
    "        print('loss:', loss)\n",
    "        \n",
    "    #对损失函数进行梯度反传\n",
    "    loss.backward()\n",
    "    \n",
    "    #利用上一步计算中得到的weights，biases等梯度信息更新weights或biases中的data数值\n",
    "    weights.data.add_(- learning_rate * weights.grad.data)  \n",
    "    biases.data.add_(- learning_rate * biases.grad.data)\n",
    "    weights2.data.add_(- learning_rate * weights2.grad.data)\n",
    "    \n",
    "    # 清空所有变量的梯度值。\n",
    "    # 因为pytorch中backward一次梯度信息会自动累加到各个变量上，因此需要清空，否则下一次迭代会累加，造成很大的偏差\n",
    "    weights.grad.data.zero_()\n",
    "    biases.grad.data.zero_()\n",
    "    weights2.grad.data.zero_()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印误差曲线\n",
    "plt.plot(losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = x.data.numpy() # 获得x包裹的数据\n",
    "plt.figure(figsize = (10, 7)) #设定绘图窗口大小\n",
    "xplot, = plt.plot(x_data, y.data.numpy(), 'o') # 绘制原始数据\n",
    "\n",
    "yplot, = plt.plot(x_data, predictions.data.numpy())  #绘制拟合数据\n",
    "plt.xlabel('X') #更改坐标轴标注\n",
    "plt.ylabel('Y') #更改坐标轴标注\n",
    "plt.legend([xplot, yplot],['Data', 'Prediction under 1000000 epochs']) #绘制图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 改进版本\n",
    "\n",
    "上面的程序之所以跑得很慢，是因为x的取值范围1～50。\n",
    "而由于所有权重和biases的取值范围被设定为-1,1的正态分布随机数，这样就导致\n",
    "我们输入给隐含层节点的数值范围为-50~50，\n",
    "要想将sigmoid函数的多个峰值调节到我们期望的位置需要耗费很多的计算时间\n",
    "\n",
    "我们的解决方案就是将输入变量的范围归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: tensor(2275.4805, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9784, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9673, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9610, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9575, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9555, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9542, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9534, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9532, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9528, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9525, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9524, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9522, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9521, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9519, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9518, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9516, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9515, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9515, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9511, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9510, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9511, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9510, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9509, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9508, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9506, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9506, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9505, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9503, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9505, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9503, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9501, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9500, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9500, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9499, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9497, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9496, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9498, grad_fn=<MeanBackward1>)\n",
      "loss: tensor(1010.9496, grad_fn=<MeanBackward1>)\n",
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     "output_type": "stream",
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    }
   ],
   "source": [
    "#取出最后一列的前50条记录来进行预测\n",
    "counts = rides['cnt'][:50]\n",
    "\n",
    "#创建归一化的变量x，它的取值是0.02,0.04,...,1\n",
    "x = torch.FloatTensor(np.arange(len(counts), dtype = float) / len(counts))\n",
    "\n",
    "# 创建归一化的预测变量y，它的取值范围是0～1\n",
    "y = torch.FloatTensor(np.array(counts, dtype = float))\n",
    "\n",
    "# 初始化所有神经网络的权重（weights）和阈值（biases）\n",
    "weights = torch.randn((1, sz), requires_grad = True)#1*10的输入到隐含层的权重矩阵\n",
    "biases = torch.randn((sz), requires_grad = True) #尺度为10的隐含层节点偏置向量\n",
    "weights2 = torch.randn((sz, 1), requires_grad = True) #10*1的隐含到输出层权重矩阵\n",
    "\n",
    "learning_rate = 0.0001 #设置学习率\n",
    "losses = []\n",
    "for i in range(2000000):\n",
    "    # 从输入层到隐含层的计算\n",
    "    hidden = x.expand(sz, len(x)).t() * weights.expand(len(x), sz) + biases.expand(len(x), sz)\n",
    "    # 将sigmoid函数作用在隐含层的每一个神经元上\n",
    "    hidden = torch.sigmoid(hidden)\n",
    "    # 隐含层输出到输出层，计算得到最终预测\n",
    "    predictions = hidden.mm(weights2)# + biases2.expand_as(y)\n",
    "    # 通过与标签数据y比较，计算均方误差\n",
    "    loss = torch.mean((predictions - y) ** 2) \n",
    "    losses.append(loss.data.numpy())\n",
    "    \n",
    "    # 每隔10000个周期打印一下损失函数数值\n",
    "    if i % 10000 == 0:\n",
    "        print('loss:', loss)\n",
    "        \n",
    "    #对损失函数进行梯度反传\n",
    "    loss.backward()\n",
    "    \n",
    "    #利用上一步计算中得到的weights，biases等梯度信息更新weights或biases中的data数值\n",
    "    weights.data.add_(- learning_rate * weights.grad.data)  \n",
    "    biases.data.add_(- learning_rate * biases.grad.data)\n",
    "    weights2.data.add_(- learning_rate * weights2.grad.data)\n",
    "    \n",
    "    # 清空所有变量的梯度值。\n",
    "    # 因为pytorch中backward一次梯度信息会自动累加到各个变量上，因此需要清空，否则下一次迭代会累加，造成很大的偏差\n",
    "    weights.grad.data.zero_()\n",
    "    biases.grad.data.zero_()\n",
    "    weights2.grad.data.zero_()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(losses)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_data = x.data.numpy() # 获得x包裹的数据\n",
    "plt.figure(figsize = (10, 7)) #设定绘图窗口大小\n",
    "xplot, = plt.plot(x_data, y.data.numpy(), 'o') # 绘制原始数据\n",
    "yplot, = plt.plot(x_data, predictions.data.numpy())  #绘制拟合数据\n",
    "plt.xlabel('X') #更改坐标轴标注\n",
    "plt.ylabel('Y') #更改坐标轴标注\n",
    "plt.legend([xplot, yplot],['Data', 'Prediction']) #绘制图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 用训练好的神经网络做预测\n",
    "\n",
    "预测下50个节点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(3947.3535, grad_fn=<MeanBackward1>)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts_predict = rides['cnt'][50:100] #读取待预测的接下来的50个数据点\n",
    "\n",
    "#首先对接下来的50个数据点进行选取，注意x应该取51，52，……，100，然后再归一化\n",
    "x = torch.FloatTensor((np.arange(len(counts_predict), dtype = float) + len(counts)) / len(counts_predict))\n",
    "\n",
    "#读取下50个点的y数值，不需要做归一化\n",
    "y = torch.FloatTensor(np.array(counts_predict, dtype = float))\n",
    "\n",
    "# 从输入层到隐含层的计算\n",
    "hidden = x.expand(sz, len(x)).t() * weights.expand(len(x), sz) + biases.expand(len(x), sz)\n",
    "\n",
    "# 将sigmoid函数作用在隐含层的每一个神经元上\n",
    "hidden = torch.sigmoid(hidden)\n",
    "\n",
    "# 隐含层输出到输出层，计算得到最终预测\n",
    "predictions = hidden.mm(weights2)\n",
    "\n",
    "# 计算预测数据上的损失函数\n",
    "loss = torch.mean((predictions - y) ** 2) \n",
    "print(loss)\n",
    "\n",
    "\n",
    "x_data = x.data.numpy() # 获得x包裹的数据\n",
    "plt.figure(figsize = (10, 7)) #设定绘图窗口大小\n",
    "xplot, = plt.plot(x_data, y.data.numpy(), 'o') # 绘制原始数据\n",
    "yplot, = plt.plot(x_data, predictions.data.numpy())  #绘制拟合数据\n",
    "plt.xlabel('X') #更改坐标轴标注\n",
    "plt.ylabel('Y') #更改坐标轴标注\n",
    "plt.legend([xplot, yplot],['Data', 'Prediction']) #绘制图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然而，预测发现存在着非常严重的过拟合现象！原因是x和y根本就没有关系！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、人工神经网络Neu\n",
    "\n",
    "在这一小节中，我们将再构建一个人工神经网络，利用数据库中的星期几、节假日、时间、风速等信息预测共享单车的使用数量\n",
    "\n",
    "该神经网络有56个输入层节点、10个隐含层节点和1个输出节点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 数据的预处理过程\n",
    "\n",
    "要读入其他的数据就要考虑到这些数据具有不同的数据类型，以及取值范围，所以要对它们进行预处理\n",
    "\n",
    "另外，由于我们利用了全部数据来训练神经网络，所以采用之前介绍的一次性在全部数据上训练网络的方法就会很慢，\n",
    "所以我们将数据划分成了不同的撮（batch），一个批次一个批次地训练神经网络，因此我们要对数据进行划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
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       "      <td>0.24</td>\n",
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       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>13</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
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       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.22</td>\n",
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       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>32</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2011-01-01</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#首先，让我们再来看看数据长什么样子\n",
    "#读取数据到内存中，rides为一个dataframe对象\n",
    "data_path = 'bike-sharing-dataset/hour.csv'\n",
    "rides = pd.read_csv(data_path)\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. 对于类型变量的处理\n",
    "\n",
    "有很多变量都属于类型变量，例如season=1,2,3,4，分四季。我们不能将season变量直接输入到神经网络，这是因为season数值越高并不表示相应的信号强度越大。我们的解决方案是将类型变量用一个“一位热码“（one-hot）来编码，也就是：\n",
    "\n",
    "$\n",
    "season = 1 \\rightarrow (1, 0, 0 ,0) \\\\\n",
    "season = 2 \\rightarrow (0, 1, 0, 0) \\\\\n",
    "season = 3 \\rightarrow (0, 0, 1, 0) \\\\\n",
    "season = 4 \\rightarrow (0, 0, 0, 1) \\\\\n",
    "$\n",
    "\n",
    "因此，如果一个类型变量有n个不同取值，那么我们的“一位热码“所对应的向量长度就为n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>windspeed</th>\n",
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       "<p>5 rows × 59 columns</p>\n",
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      "text/plain": [
       "   yr  holiday  temp   hum  windspeed  casual  registered  cnt  season_1  \\\n",
       "0   0        0  0.24  0.81        0.0       3          13   16         1   \n",
       "1   0        0  0.22  0.80        0.0       8          32   40         1   \n",
       "2   0        0  0.22  0.80        0.0       5          27   32         1   \n",
       "3   0        0  0.24  0.75        0.0       3          10   13         1   \n",
       "4   0        0  0.24  0.75        0.0       0           1    1         1   \n",
       "\n",
       "   season_2    ...      hr_21  hr_22  hr_23  weekday_0  weekday_1  weekday_2  \\\n",
       "0         0    ...          0      0      0          0          0          0   \n",
       "1         0    ...          0      0      0          0          0          0   \n",
       "2         0    ...          0      0      0          0          0          0   \n",
       "3         0    ...          0      0      0          0          0          0   \n",
       "4         0    ...          0      0      0          0          0          0   \n",
       "\n",
       "   weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          1  \n",
       "1          0          0          0          1  \n",
       "2          0          0          0          1  \n",
       "3          0          0          0          1  \n",
       "4          0          0          0          1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对于类型变量的特殊处理\n",
    "# season=1,2,3,4, weathersi=1,2,3, mnth= 1,2,...,12, hr=0,1, ...,23, weekday=0,1,...,6\n",
    "# 经过下面的处理后，将会多出若干特征，例如，对于season变量就会有 season_1, season_2, season_3, season_4\n",
    "# 这四种不同的特征。\n",
    "dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
    "for each in dummy_fields:\n",
    "    #利用pandas对象，我们可以很方便地将一个类型变量属性进行one-hot编码，变成多个属性\n",
    "    dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
    "    rides = pd.concat([rides, dummies], axis=1)\n",
    "\n",
    "# 把原有的类型变量对应的特征去掉，将一些不相关的特征去掉\n",
    "fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
    "                  'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
    "data = rides.drop(fields_to_drop, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. 对于数值类型变量进行标准化\n",
    "由于每个数值型变量都是相互独立的，所以它们的数值绝对大小与问题本身没有关系，为了消除数值大小的差异，我们对每一个数值型变量进行标准化处理，也就是让其数值都围绕着0左右波动。比如，对于温度temp这个变量来说，它在整个数据库取值的平均着为mean(temp), 方差为std(temp)，所以，归一化的温度计算为：\n",
    "\n",
    "$ temp'=\\frac{temp - mean(temp)}{std(temp)}$\n",
    "\n",
    "这样做的好处就是可以将不同的取值范围的变量设置为让它们处于一个平等的地位。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调整所有的特征，标准化处理\n",
    "quant_features = ['cnt', 'temp', 'hum', 'windspeed']\n",
    "#quant_features = ['temp', 'hum', 'windspeed']\n",
    "\n",
    "# 我们将每一个变量的均值和方差都存储到scaled_features变量中。\n",
    "scaled_features = {}\n",
    "for each in quant_features:\n",
    "    mean, std = data[each].mean(), data[each].std()\n",
    "    scaled_features[each] = [mean, std]\n",
    "    data.loc[:, each] = (data[each] - mean)/std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### c. 将数据集进行分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据： 16875 测试数据： 504\n"
     ]
    }
   ],
   "source": [
    "# 将所有的数据集分为测试集和训练集，我们以后21天数据一共21*24个数据点作为测试集，其它是训练集\n",
    "test_data = data[-21*24:]\n",
    "train_data = data[:-21*24]\n",
    "print('训练数据：',len(train_data),'测试数据：',len(test_data))\n",
    "\n",
    "# 将我们的数据列分为特征列和目标列\n",
    "\n",
    "#目标列\n",
    "target_fields = ['cnt', 'casual', 'registered']\n",
    "features, targets = train_data.drop(target_fields, axis=1), train_data[target_fields]\n",
    "test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]\n",
    "\n",
    "# 将数据从pandas dataframe转换为numpy\n",
    "X = features.values\n",
    "Y = targets['cnt'].values\n",
    "Y = Y.astype(float)\n",
    "\n",
    "Y = np.reshape(Y, [len(Y),1])\n",
    "losses = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   yr  holiday      temp       hum  windspeed  season_1  season_2  season_3  \\\n",
       "0   0        0 -1.334609  0.947345  -1.553844         1         0         0   \n",
       "1   0        0 -1.438475  0.895513  -1.553844         1         0         0   \n",
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       "4   0        0 -1.334609  0.636351  -1.553844         1         0         0   \n",
       "\n",
       "   season_4  weathersit_1    ...      hr_21  hr_22  hr_23  weekday_0  \\\n",
       "0         0             1    ...          0      0      0          0   \n",
       "1         0             1    ...          0      0      0          0   \n",
       "2         0             1    ...          0      0      0          0   \n",
       "3         0             1    ...          0      0      0          0   \n",
       "4         0             1    ...          0      0      0          0   \n",
       "\n",
       "   weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \n",
       "0          0          0          0          0          0          1  \n",
       "1          0          0          0          0          0          1  \n",
       "2          0          0          0          0          0          1  \n",
       "3          0          0          0          0          0          1  \n",
       "4          0          0          0          0          0          1  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 构建神经网络并进行训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a. 手动编写用Tensor运算的人工神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义神经网络架构，features.shape[1]个输入层单元，10个隐含层，1个输出层\n",
    "input_size = features.shape[1] #输入层单元个数\n",
    "hidden_size = 10 #隐含层单元个数\n",
    "output_size = 1 #输出层单元个数\n",
    "batch_size = 128 #每隔batch的记录数\n",
    "weights1 = torch.randn(([input_size, hidden_size]), requires_grad = True) #第一到二层权重\n",
    "biases1 = torch.randn(([hidden_size]), requires_grad = True) #隐含层偏置\n",
    "weights2 = torch.randn(([hidden_size, output_size]), requires_grad = True) #隐含层到输出层权重\n",
    "def neu(x):\n",
    "    #计算隐含层输出\n",
    "    #x为batch_size * input_size的矩阵，weights1为input_size*hidden_size矩阵，\n",
    "    #biases为hidden_size向量，输出为batch_size * hidden_size矩阵    \n",
    "    hidden = x.mm(weights1) + biases1.expand(x.size()[0], hidden_size)\n",
    "    hidden = torch.sigmoid(hidden)\n",
    "    \n",
    "    #输入batch_size * hidden_size矩阵，mm上weights2, hidden_size*output_size矩阵，\n",
    "    #输出batch_size*output_size矩阵\n",
    "    output = hidden.mm(weights2)\n",
    "    return output\n",
    "def cost(x, y):\n",
    "    # 计算损失函数\n",
    "    error = torch.mean((x - y)**2)\n",
    "    return error\n",
    "def zero_grad():\n",
    "    # 清空每个参数的梯度信息\n",
    "    if weights1.grad is not None and biases1.grad is not None and weights2.grad is not None:\n",
    "        weights1.grad.data.zero_()\n",
    "        weights2.grad.data.zero_()\n",
    "        biases1.grad.data.zero_()\n",
    "def optimizer_step(learning_rate):\n",
    "    # 梯度下降算法\n",
    "    weights1.data.add_(- learning_rate * weights1.grad.data)\n",
    "    weights2.data.add_(- learning_rate * weights2.grad.data)\n",
    "    biases1.data.add_(- learning_rate * biases1.grad.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1.7810287\n",
      "100 0.4350619\n",
      "200 0.29341227\n",
      "300 0.25225744\n",
      "400 0.21819906\n",
      "500 0.16945766\n",
      "600 0.12700191\n",
      "700 0.10736521\n",
      "800 0.0990122\n",
      "900 0.09448865\n"
     ]
    }
   ],
   "source": [
    "# 神经网络训练循环\n",
    "losses = []\n",
    "for i in range(1000):\n",
    "    # 每128个样本点被划分为一个撮，在循环的时候一批一批地读取\n",
    "    batch_loss = []\n",
    "    # start和end分别是提取一个batch数据的起始和终止下标\n",
    "    for start in range(0, len(X), batch_size):\n",
    "        end = start + batch_size if start + batch_size < len(X) else len(X)\n",
    "        xx = torch.FloatTensor(X[start:end])\n",
    "        yy = torch.FloatTensor(Y[start:end])\n",
    "        predict = neu(xx)\n",
    "        loss = cost(predict, yy)\n",
    "        zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer_step(0.01)\n",
    "        batch_loss.append(loss.data.numpy())\n",
    "    \n",
    "    # 每隔100步输出一下损失值（loss）\n",
    "    if i % 100==0:\n",
    "        losses.append(np.mean(batch_loss))\n",
    "        print(i, np.mean(batch_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'MSE')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印输出损失值\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "plt.plot(np.arange(len(losses))*100,losses, 'o-')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('MSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### b. 调用PyTorch现成的函数，构建序列化的神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义神经网络架构，features.shape[1]个输入层单元，10个隐含层，1个输出层\n",
    "input_size = features.shape[1]\n",
    "hidden_size = 10\n",
    "output_size = 1\n",
    "batch_size = 128\n",
    "neu = torch.nn.Sequential(\n",
    "    torch.nn.Linear(input_size, hidden_size),\n",
    "    torch.nn.Sigmoid(),\n",
    "    torch.nn.Linear(hidden_size, output_size),\n",
    ")\n",
    "cost = torch.nn.MSELoss()\n",
    "optimizer = torch.optim.SGD(neu.parameters(), lr = 0.01)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.94799244\n",
      "100 0.27580827\n",
      "200 0.25195518\n",
      "300 0.20748073\n",
      "400 0.13288398\n",
      "500 0.09238259\n",
      "600 0.07558619\n",
      "700 0.06834076\n",
      "800 0.064559445\n",
      "900 0.062239822\n"
     ]
    }
   ],
   "source": [
    "# 神经网络训练循环\n",
    "losses = []\n",
    "for i in range(1000):\n",
    "    # 每128个样本点被划分为一个撮，在循环的时候一批一批地读取\n",
    "    batch_loss = []\n",
    "    # start和end分别是提取一个batch数据的起始和终止下标\n",
    "    for start in range(0, len(X), batch_size):\n",
    "        end = start + batch_size if start + batch_size < len(X) else len(X)\n",
    "        xx = torch.FloatTensor(X[start:end])\n",
    "        yy = torch.FloatTensor(Y[start:end])\n",
    "        predict = neu(xx)\n",
    "        loss = cost(predict, yy)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        batch_loss.append(loss.data.numpy())\n",
    "    \n",
    "    # 每隔100步输出一下损失值（loss）\n",
    "    if i % 100==0:\n",
    "        losses.append(np.mean(batch_loss))\n",
    "        print(i, np.mean(batch_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'MSE')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印输出损失值\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "plt.plot(np.arange(len(losses))*100,losses, 'o-')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('MSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 测试神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 用训练好的神经网络在测试集上进行预测\n",
    "targets = test_targets['cnt'] #读取测试集的cnt数值\n",
    "targets = targets.values.reshape([len(targets),1]) #将数据转换成合适的tensor形式\n",
    "targets = targets.astype(float) #保证数据为实数\n",
    "\n",
    "# 将属性和预测变量包裹在Variable型变量中\n",
    "x = torch.FloatTensor(test_features.values)\n",
    "y = torch.FloatTensor(targets)\n",
    "\n",
    "# 用神经网络进行预测\n",
    "predict = neu(x)\n",
    "predict = predict.data.numpy()\n",
    "\n",
    "\n",
    "# 将后21天的预测数据与真实数据画在一起并比较\n",
    "# 横坐标轴是不同的日期，纵坐标轴是预测或者真实数据的值\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "\n",
    "mean, std = scaled_features['cnt']\n",
    "ax.plot(predict * std + mean, label='Prediction', linestyle = '--')\n",
    "ax.plot(targets * std + mean, label='Data', linestyle = '-')\n",
    "ax.legend()\n",
    "ax.set_xlabel('Date-time')\n",
    "ax.set_ylabel('Counts')\n",
    "# 对横坐标轴进行标注\n",
    "dates = pd.to_datetime(rides.loc[test_data.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 诊断网络*\n",
    "\n",
    "在这一小节我们对网络出现的问题进行诊断，看看哪一些神经元导致了预测偏差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 选出三天预测不准的日期：Dec 22，23，24\n",
    "# 将这三天的数据聚集到一起，存入subset和subtargets中\n",
    "bool1 = rides['dteday'] == '2012-12-22'\n",
    "bool2 = rides['dteday'] == '2012-12-23'\n",
    "bool3 = rides['dteday'] == '2012-12-24'\n",
    "\n",
    "# 将三个布尔型数组求与\n",
    "bools = [any(tup) for tup in zip(bool1,bool2,bool3) ]\n",
    "# 将相应的变量取出来\n",
    "subset = test_features.loc[rides[bools].index]\n",
    "subtargets = test_targets.loc[rides[bools].index]\n",
    "subtargets = subtargets['cnt']\n",
    "subtargets = subtargets.values.reshape([len(subtargets),1])\n",
    "\n",
    "def feature(X, net):\n",
    "    # 定义了一个函数可以提取网络的权重信息，所有的网络参数信息全部存储在了neu的named_parameters集合中了\n",
    "    X = torch.from_numpy(X).type(torch.FloatTensor)\n",
    "    dic = dict(net.named_parameters()) #提取出来这个集合\n",
    "    weights = dic['0.weight'] #可以按照层数.名称来索引集合中的相应参数值\n",
    "    biases = dic['0.bias'] #可以按照层数.名称来索引集合中的相应参数值\n",
    "    h = torch.sigmoid(X.mm(weights.t()) + biases.expand([len(X), len(biases)])) # 隐含层的计算过程\n",
    "    return h # 输出层的计算\n",
    "\n",
    "# 将这几天的数据输入到神经网络中，读取出隐含层神经元的激活数值，存入results中\n",
    "results = feature(subset.values, neu).data.numpy()\n",
    "# 这些数据对应的预测值（输出层）\n",
    "predict = neu(torch.FloatTensor(subset.values)).data.numpy()\n",
    "\n",
    "#将预测值还原成原始数据的数值范围\n",
    "mean, std = scaled_features['cnt']\n",
    "predict = predict * std + mean\n",
    "subtargets = subtargets * std + mean\n",
    "# 将所有的神经元激活水平画在同一张图上，蓝色的是模型预测的数值\n",
    "fig, ax = plt.subplots(figsize = (8, 6))\n",
    "ax.plot(results[:,:],'.:',alpha = 0.3)\n",
    "ax.plot((predict - min(predict)) / (max(predict) - min(predict)),'bs-',label='Prediction')\n",
    "ax.plot((subtargets - min(predict)) / (max(predict) - min(predict)),'ro-',label='Real')\n",
    "ax.plot(results[:, 3],':*',alpha=1, label='Neuro 4')\n",
    "\n",
    "ax.set_xlim(right=len(predict))\n",
    "ax.legend()\n",
    "plt.ylabel('Normalized Values')\n",
    "\n",
    "dates = pd.to_datetime(rides.loc[subset.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Weight')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 找到了与峰值相应的神经元，把它到输入层的权重输出出来\n",
    "dic = dict(neu.named_parameters())\n",
    "weights = dic['2.weight']\n",
    "plt.plot(weights.data.numpy()[0],'o-')\n",
    "plt.xlabel('Input Neurons')\n",
    "plt.ylabel('Weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Weight')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 找到了与峰值响应的神经元，把它到输入层的权重输出出来\n",
    "dic = dict(neu.named_parameters())\n",
    "weights = dic['0.weight']\n",
    "plt.plot(weights.data.numpy(),'o-')\n",
    "plt.xlabel('Input Neurons')\n",
    "plt.ylabel('Weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 yr\n",
      "1 holiday\n",
      "2 temp\n",
      "3 hum\n",
      "4 windspeed\n",
      "5 season_1\n",
      "6 season_2\n",
      "7 season_3\n",
      "8 season_4\n",
      "9 weathersit_1\n",
      "10 weathersit_2\n",
      "11 weathersit_3\n",
      "12 weathersit_4\n",
      "13 mnth_1\n",
      "14 mnth_2\n",
      "15 mnth_3\n",
      "16 mnth_4\n",
      "17 mnth_5\n",
      "18 mnth_6\n",
      "19 mnth_7\n",
      "20 mnth_8\n",
      "21 mnth_9\n",
      "22 mnth_10\n",
      "23 mnth_11\n",
      "24 mnth_12\n",
      "25 hr_0\n",
      "26 hr_1\n",
      "27 hr_2\n",
      "28 hr_3\n",
      "29 hr_4\n",
      "30 hr_5\n",
      "31 hr_6\n",
      "32 hr_7\n",
      "33 hr_8\n",
      "34 hr_9\n",
      "35 hr_10\n",
      "36 hr_11\n",
      "37 hr_12\n",
      "38 hr_13\n",
      "39 hr_14\n",
      "40 hr_15\n",
      "41 hr_16\n",
      "42 hr_17\n",
      "43 hr_18\n",
      "44 hr_19\n",
      "45 hr_20\n",
      "46 hr_21\n",
      "47 hr_22\n",
      "48 hr_23\n",
      "49 weekday_0\n",
      "50 weekday_1\n",
      "51 weekday_2\n",
      "52 weekday_3\n",
      "53 weekday_4\n",
      "54 weekday_5\n",
      "55 weekday_6\n"
     ]
    }
   ],
   "source": [
    "# 列出所有的features中的数据列，找到对应的编号\n",
    "for (i, c) in zip(range(len(features.columns)), features.columns):\n",
    "    print(i,c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示在不同日期，指定的第7个隐含层神经元细胞的激活值，以及输入层响应\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "ax.plot(results[:,6],label='neuron in hidden')\n",
    "ax.plot(subset.values[:,33],label='neuron in input at 8am')\n",
    "ax.plot(subset.values[:,42],label='neuron in input at 5pm')\n",
    "ax.set_xlim(right=len(predict))\n",
    "ax.legend()\n",
    "\n",
    "dates = pd.to_datetime(rides.loc[subset.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 分类人工神经网络Neuc\n",
    "\n",
    "本小节中，我们解决一个分类问题，即将预测数值根据大于或者小于预测数量的平均值来分成两类\n",
    "我们只需要对Neuc进行小小的更改，将其输出单元数量设置为2，并加上Sigmoid函数就可以了\n",
    "\n",
    "对于Neuc来说，它的输出是两个数值，分别表示属于第0类和第1类的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重新构造用于分类的人工神经网络Neuc\n",
    "\n",
    "input_size = features.shape[1]\n",
    "hidden_size = 10\n",
    "output_size = 2\n",
    "batch_size = 128\n",
    "neuc = torch.nn.Sequential(\n",
    "    torch.nn.Linear(input_size, hidden_size),\n",
    "    torch.nn.Sigmoid(),\n",
    "    torch.nn.Linear(hidden_size, output_size),\n",
    "    torch.nn.Sigmoid(),\n",
    ")\n",
    "# 将损失函数定义为交叉熵\n",
    "cost = torch.nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(neuc.parameters(), lr = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_labels = Y > np.mean(Y)\n",
    "Y_labels = Y_labels.astype(int)\n",
    "Y_labels = Y_labels.reshape(-1)\n",
    "Y_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.68776065 45.64050572519084\n",
      "100 0.44350398 12.804150763358779\n",
      "200 0.43247986 12.070610687022901\n",
      "300 0.42094445 10.764551526717558\n",
      "400 0.4089715 9.11259541984733\n",
      "500 0.39874342 8.07490458015267\n",
      "600 0.39065027 7.126669847328245\n",
      "700 0.38469765 6.6495706106870225\n",
      "800 0.38027692 6.160543893129771\n",
      "900 0.37727585 5.9100667938931295\n",
      "1000 0.37486088 5.689408396946565\n",
      "1100 0.37280318 5.534351145038168\n",
      "1200 0.37109962 5.343511450381679\n",
      "1300 0.36967543 5.218272900763359\n",
      "1400 0.36843207 5.0691793893129775\n",
      "1500 0.36733428 4.932013358778626\n",
      "1600 0.3663754 4.848520992366412\n",
      "1700 0.3655286 4.812738549618321\n",
      "1800 0.36477104 4.645753816793893\n",
      "1900 0.36408466 4.550333969465649\n",
      "2000 0.3634559 4.496660305343512\n",
      "2100 0.36287516 4.490696564885496\n",
      "2200 0.36233556 4.454914122137405\n",
      "2300 0.36183155 4.395276717557252\n",
      "2400 0.36136028 4.353530534351145\n",
      "2500 0.36092162 4.3058206106870225\n",
      "2600 0.36051458 4.258110687022901\n",
      "2700 0.36013526 4.210400763358779\n",
      "2800 0.35977906 4.168654580152672\n",
      "2900 0.3594424 4.138835877862595\n",
      "3000 0.3591224 4.1209446564885495\n",
      "3100 0.35881665 4.085162213740458\n",
      "3200 0.35852355 4.049379770992366\n",
      "3300 0.35824096 4.043416030534351\n",
      "3400 0.35796666 4.013597328244275\n",
      "3500 0.35769862 3.9897423664122136\n",
      "3600 0.35743672 3.9837786259541983\n",
      "3700 0.3571826 3.9599236641221376\n",
      "3800 0.3569375 3.9420324427480917\n",
      "3900 0.35670075 3.930104961832061\n"
     ]
    }
   ],
   "source": [
    "# 定义一个专门计算分类错误率的函数，它的基本思想是，对于预测向量predictions的每一行，\n",
    "# 取最大的那个元素的下标，与标签labels中的元素做比较\n",
    "def error_rate(predictions, labels):\n",
    "    \"\"\"计算预测错误率的函数，其中predictions是模型给出的一组预测结果，labels是数据之中的正确答案\"\"\"\n",
    "    predictions = np.argmax(predictions, 1)\n",
    "    return 100.0 - (\n",
    "      100.0 *\n",
    "      np.sum( predictions == labels) /\n",
    "      predictions.shape[0])\n",
    "\n",
    "# 神经网络训练循环\n",
    "losses = []\n",
    "errors = []\n",
    "for i in range(4000):\n",
    "    # 每128个样本点被划分为一个撮\n",
    "    batch_loss = []\n",
    "    batch_errors = []\n",
    "    for start, end in zip(range(0, len(X), batch_size), range(batch_size, len(X)+1, batch_size)):\n",
    "        xx = torch.FloatTensor(X[start:end])\n",
    "        yy = torch.LongTensor(Y_labels[start:end])\n",
    "        predict = neuc(xx)\n",
    "        loss = cost(predict, yy)\n",
    "        err = error_rate(predict.data.numpy(), yy.data.numpy())\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        batch_loss.append(loss.data.numpy())\n",
    "        batch_errors.append(err)\n",
    "    \n",
    "    # 每隔100步输出一下损失值（loss）\n",
    "    if i % 100==0:\n",
    "        losses.append(np.mean(batch_loss))\n",
    "        errors.append(np.mean(batch_errors))\n",
    "        print(i, np.mean(batch_loss), np.mean(batch_errors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2148343a4a8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 打印输出损失值\n",
    "plt.plot(np.arange(len(losses))*100,losses, label = 'Cross Entropy')\n",
    "plt.plot(np.arange(len(losses))*100, np.array(errors) / float(100), label = 'Error Rate')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('Cross Entropy/Error rates')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对分类效果进行测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.492063492063494\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 读取测试数据\n",
    "targets = test_targets['cnt']\n",
    "targets = targets.values.reshape([len(targets), 1])\n",
    "Y_labels = targets > np.mean(Y)\n",
    "Y_labels = Y_labels.astype(int)\n",
    "Y_labels = Y_labels.reshape(-1)\n",
    "x = torch.FloatTensor(test_features.values)\n",
    "\n",
    "# 打印神经网络预测的错误率\n",
    "predict = neuc(x)\n",
    "print(error_rate(predict.data.numpy(), Y_labels))\n",
    "\n",
    "# 接下来，我们把预测正确的数据和错误的数据分别画出来，纵坐标分别是预测正确的概率和预测错误的概率\n",
    "prob = predict.data.numpy()\n",
    "rights = np.argmax(prob, 1) == Y_labels\n",
    "wrongs = np.argmax(prob, 1) != Y_labels\n",
    "right_labels = Y_labels[rights]\n",
    "wrong_labels = Y_labels[wrongs]\n",
    "probs = prob[rights, :]\n",
    "probs1 = prob[wrongs, :]\n",
    "rightness = [probs[i, right_labels[i]] for i in range(len(right_labels))]\n",
    "right_index = np.arange(len(targets))[rights]\n",
    "wrongness = [probs1[i, wrong_labels[i]] for i in range(len(wrong_labels))]\n",
    "wrong_index = np.arange(len(targets))[wrongs]\n",
    "fig, ax = plt.subplots(figsize = (8, 6))\n",
    "ax.plot(right_index, rightness, '.', label='Right')\n",
    "ax.plot(wrong_index, wrongness,'o',label='Wrong')\n",
    "\n",
    "ax.legend()\n",
    "plt.ylabel('Probabilities')\n",
    "\n",
    "dates = pd.to_datetime(rides.loc[test_features.index]['dteday'])\n",
    "dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
    "ax.set_xticks(np.arange(len(dates))[12::24])\n",
    "_ = ax.set_xticklabels(dates[12::24], rotation=45)"
   ]
  }
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